Neural network application in predicting advanced manufacturing technology implementation performance

Advanced Manufacturing Technology (AMT) adoption can be complex, costly, and risky. Companies need to assess and evaluate their current conditions with that of AMT requirements to identify the gaps and predict their performance. Such an approach will facilitate companies not only in their investment decisions, but on the actions needed to improve performance. The lack of such an approach prompted this study to develop an Artificial Neural Network (ANN) classification and prediction model that can assist companies especially Small and Medium size Enterprises (SMEs) in evaluating AMT implementation. Data were collected from a survey of 140 SMEs. Using cluster analysis, the companies were classified into three groups based on their performance. Then, a feed-forward NN was developed and trained with back-propagation algorithm. The results showed that the model can classify companies with 72% accuracy rate into the three clusters. This model is suitable to evaluate AMTs implementation outcomes and predict company performance as high, low, or poor in technology adoption.